Sorry in advance for the massive amount of files.  The MNP regressions and simulations are very computing intensive.
At times, I had to break things apart and run things on Pitt's cluster computers.  I also included the R image contained the 
regression results from the paper, since they are Bayesian and exact results depend on the seed set.

If you are working with Chad Bown's data, I also have all of the .do files I used to expand, set up, and merge that data.
Email me if you would like this.

If you have any questions, I can be reached permanently at: stephen.chaudoin@gmail.com



Cox Regressions
	- The dataset for the Cox regressions is: WTO_cox.csv
	- The commands to run them are in: coxR_032112.R

MNP Regressions
	- The commands for the MNP regressions are in MNP_2013_07_29.R
	- The dataset for these regressions is: WTO_MNP_2013_03_04.csv
		- Note that these are Bayesian, so you may get different results because of the seed set
		- Note too that the MNP package sometimes returns error messages where the algorithm tries to draw values outside of the allowed range.
			- Per Kosuke's advice, when this happened, I made the priors slightly more informative, picked a new seed and reran the regression.
			- (This is actually why I don't have the original seeds used-- my mistake, as a Bayesian noob at the time.)

Generating the simulations for the substantive effects:
	- You need to load the R image that contains the regression results from the paper: Interact_031412_long.RData
	- The commands to generate the simulations are in: SubsFX_2013_03_05.R

To make the substantive effects figures, you need the following R datasets, which contain the simulation results:
	- Interact_2013_03_30_long_IM_1.Rdata
	- Interact_2013_03_30_long_EX_1.Rdata
	- Interact_2013_03_30_long_PEUE1_1.Rdata
	- Interact_2013_03_30_long_PEUE0_0.Rdata
		- They are called by and the figures made in: SubsFX_FromCluster_2013_04_01.R